Low-dimensionality carbon-based biosensors: the new era of emerging technologies in bioanalytical chemistry

Author(s)

Castro, Karla P. R. ; Colombo, Rafael N. P. ; Iost, Rodrigo M. M. ; da Silva, Beatriz G. R. ; Crespilho, Frank N. N.

• Document type: Journal article
• Source: ANALYTICAL AND BIOANALYTICAL CHEMISTRY; v. N/A, p. 17-pg., 2023-02-09.

Abstract

Since the last decade, carbon nanomaterials have had a notable impact on different fields such as bioimaging, drug delivery, artificial tissue engineering, and biosensors. This is due to their good compatibility toward a wide range of chemical to biological molecules, low toxicity, and tunable properties. Especially for biosensor technology, the characteristic features of each dimensionality of carbon-based materials may influence the performance and viability of their use. Surface area, porous network, hybridization, functionalization, synthesis route, the combination of dimensionalities, purity levels, and the mechanisms underlying carbon nanomaterial interactions influence their applications in bioanalytical chemistry. Efforts are being made to fully understand how nanomaterials can influence biological interactions, to develop commercially viable biosensors, and to gain knowledge on the biomolecular processes associated with carbon. Here, we present a comprehensive review highlighting the characteristic features of the dimensionality of carbon-based materials in biosensing. (AU)

• FAPESP's process: 22/09164-5 - Understanding the charge transfer mechanisms in biological and biomimetic systems using in-situ and operando techniques: from fundamental science to energy application
• Grantee: Frank Nelson Crespilho
• Support Opportunities: Regular Research Grants
• FAPESP's process: 21/05665-7 - Development of Van der Waals surfaces for application in biodevices
• Grantee: Rafael Neri Prystaj Colombo
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis
• Grantee: Osvaldo Novais de Oliveira Junior
• Support Opportunities: Research Projects - Thematic Grants